Intoduction

At the end of 2019, a novel coronavirus was identified as the cause of a cluster of pneumonia cases in China. It rapidly spread, resulting in an epidemic throughout the world. In February 2020, the World Health Organization designated the disease COVID-19, which stands for coronavirus disease 2019. This report is designed to explore the association beween Covid-19 death and state, age groups.

Methods

The data was obstained from the US CDC, Centers of Disease Control and Prevention. The date include deaths involving coronavirus disease 2019 (COVID-19), pneumonia, and influenza reported to NCHS by sex and age group and state.

We calculate the proportionate mortality ratio due to COVID-19 by using the COVID-19 deaths/total deaths.

library(data.table)
library(dtplyr)
library(dplyr)
library(leaflet)
library(tidyverse)
library(ggplot2)
covid = fread("data/Provisional_COVID-19_Death_Counts_by_Sex__Age__and_State.csv")

#calculate proportionate mortality ratio due to COVID-19
covid$PMR=covid$`COVID-19 Deaths`/covid$`Total Deaths`

Compare the proportionate mortality ratio among states.

Here, we create a map of proportionate mortality ratio among the states.

covid_state=covid[which(covid$Sex == "All Sexes" & covid$State != "United States" & covid$`Age group`=="All Ages"),]


#covid_state$State[which(covid_state$State %in% states$NAME ==F)]


covid_state[33,7:12]=covid_state[33,7:12]+covid_state[34,7:12]
covid_state=covid_state[-34, ]
covid_state$PMR=covid_state$`COVID-19 Deaths`/covid_state$`Total Deaths`
colnames(covid_state)[4]="NAME"


mergedata=merge(x = states, y = covid_state, by = "NAME", all.x = TRUE)


pal <- colorBin("YlOrRd", domain = mergedata$PMR)


leaflet() %>%
 addProviderTiles("CartoDB.Positron") %>%
  setView(-98.483330, 38.712046, zoom = 4) %>% 
  addPolygons(
    data=mergedata,
    fillColor = ~pal(mergedata$PMR),
    fillOpacity = 0.7, 
              weight = 0.2, 
              smoothFactor = 0.2 
  )%>%
  addLegend(pal = pal, 
            values = mergedata$PMR, 
            position = "bottomright", 
            title = "PMR")

Here we can see that the New York State has the highest proportionate mortality ratio due to COVID-19. It means that New York City has the most people died because of Covid-19.

Compare the proportionate mortality ratio beween genders and age groups.

covid_2=covid[which(covid$Sex == "All Sexes" & covid$State == "United States" & covid$`Age group`!="All Ages"),]
covid_2=covid_2[which(covid_2$`Age group` == "Under 1 year" | covid$`Age group` == "1-4 years" | covid$`Age group` == "5-14 years" | covid$`Age group` == "15-24 years" | covid$`Age group` == "25-34 years" | covid$`Age group` == "35-44 years" | covid$`Age group` == "45-54 years" | covid$`Age group` == "55-64 years" | covid$`Age group` == "65-74 years" | covid$`Age group` == "75-84 years" | covid$`Age group` == "85 years and over")]

covid_2=na.omit(covid_2)

covid_3=subset(covid_2, select = c("Sex", "Age group","COVID-19 Deaths","Total Deaths","PMR"))
                
knitr::kable(covid_3)
Sex Age group COVID-19 Deaths Total Deaths PMR
All Sexes Under 1 year 22 12092 0.0018194
All Sexes 5-14 years 35 3597 0.0097303
All Sexes 15-24 years 369 23153 0.0159375
All Sexes 18-29 years 889 41027 0.0216687
All Sexes 30-49 years 9181 142650 0.0643603
All Sexes 45-54 years 10627 123171 0.0862784
All Sexes 50-64 years 31899 355464 0.0897390
All Sexes 65-74 years 42950 426332 0.1007431
All Sexes 75-84 years 52618 519488 0.1012882
All Sexes 85 years and over 61172 643778 0.0950203

Conlucsion

From the map we can conlucde that the New York State has the highest proportionate mortality ratio due to COVID-19. We found that older people (greater than 65 years old) are easier die because of Covid-19.